zml
Any model. Any hardware. Zero compromise. Built with @ziglang / @openxla / MLIR / @bazelbuild
Stars: 2701
ZML is a high-performance AI inference stack built for production, using Zig language, MLIR, and Bazel. It allows users to create exciting AI projects, run pre-packaged models like MNIST, TinyLlama, OpenLLama, and Meta Llama, and compile models for accelerator runtimes. Users can also run tests, explore examples, and contribute to the project. ZML is licensed under the Apache 2.0 license.
README:
At ZML, we are creating exciting AI products on top of our high-performance AI inference stack. Our stack is built for production, using the amazing Zig language, MLIR, and the power of Bazel.
We're very happy to share our inference stack with the World and hope it allows you, too, to build cool and exciting AI projects.
To give you a glimpse of what you can do with ZML, here is an early demo:
It shows a prototype running a LLama3 model sharded on 1 NVIDIA RTX 4090, 1 AMD 6800XT, and 1 Google Cloud TPU v2. All accelerators were hosted in different locations, with activations being passed over a VPN.
All processes used the same model code, cross-compiled on a Mac, and copied onto the servers.
For more inspiration, see also the examples below or check out the examples folder.
We use bazel to build ZML and its dependencies. The only prerequisite is
bazel, which we recommend to download through bazelisk, a version manager
for bazel.
Please note: If you do not wish to install bazel system-wide, we provide
bazel.sh which downloads it to your home folder
and runs it.
Install Bazel (recommended):
curl -L -o /usr/local/bin/bazel 'https://github.com/bazelbuild/bazelisk/releases/download/v1.25.0/bazelisk-linux-amd64'
chmod +x /usr/local/bin/bazel
We have implemented a variety of example models in ZML. See our reference implementations in the examples folder.
The classic handwritten digits
recognition task. The model is tasked to recognize a handwritten digit, which
has been converted to a 28x28 pixel monochrome image. Bazel will download a
pre-trained model, and the test dataset. The program will load the model,
compile it, and classify a randomly picked example from the test dataset.
On the command line:
bazel run --config=release //examples/mnist
# or
./bazel.sh run --config=release //examples/mnist
This model has restrictions, see here. It requires approval from Meta on Huggingface, which can take a few hours to get granted.
Once you've been granted access, you're ready to download a gated model like
Llama-3.1-8B-Instruct!
First, you need to download the model using the huggingface-cli.
Note you don't need to install it yourself,
you can just use the packaged version bazel run //tools/hf --.
bazel run //tools/hf -- download meta-llama/Llama-3.1-8B-Instruct --local-dir $HOME/Llama-3.1-8B-Instruct --exclude='*.pth'
Then, you can run the model.
bazel run --config=release //examples/llama -- --hf-model-path=$HOME/Llama-3.1-8B-Instruct
bazel run --config=release //examples/llama -- --hf-model-path=$HOME/Llama-3.1-8B-Instruct --prompt="What is the capital of France?"
You can also try Llama-3.1-70B-Instruct if you have enough memory.
Like the 8B model above, this model also requires approval. See here for access requirements.
bazel run //tools/hf -- download meta-llama/Llama-3.2-1B-Instruct --local-dir $HOME/Llama-3.2-1B-Instruct --exclude='*.pth'
bazel run --config=release //examples/llama -- --hf-model-path=$HOME/Llama-3.2-1B-Instruct
bazel run --config=release //examples/llama -- --hf-model-path=$HOME/Llama-3.2-1B-Instruct -- --prompt="What is the capital of France?"
For a larger 3.2 model, you can also try Llama-3.2-3B-Instruct.
You can compile models for accelerator runtimes by appending one or more of the following arguments to the command line when compiling / running a model:
- NVIDIA CUDA:
--@zml//runtimes:cuda=true - AMD RoCM:
--@zml//runtimes:rocm=true - Google TPU:
--@zml//runtimes:tpu=true - AWS Trainium/Inferentia 2:
--@zml//runtimes:neuron=true -
AVOID CPU:
--@zml//runtimes:cpu=false
The latter, avoiding compilation for CPU, cuts down compilation time.
So, to run the Llama 3.1 8B model from above on your host sporting an NVIDIA GPU, run the following:
bazel run --config=release //examples/llama \
--@zml//runtimes:cuda=true \
-- --hf-model-path=$HOME/Llama-3.1-8B-Instruct \
--prompt="What is the capital of France?"
bazel test //zml:test
const std = @import("std");
const zml = @import("zml");
/// Model definition
const Mnist = struct {
fc1: Layer,
fc2: Layer,
const Layer = struct {
weight: zml.Tensor,
bias: zml.Tensor,
pub fn forward(self: Layer, input: zml.Tensor) zml.Tensor {
return self.weight.matmul(input).add(self.bias).relu();
}
};
/// just two linear layers + relu activation
pub fn forward(self: Mnist, input: zml.Tensor) zml.Tensor {
std.log.info("Compiling for target: {s}", .{@tagName(input.getContext().target())});
var x = input.flattenAll().convert(.f32);
const layers: []const Layer = &.{ self.fc1, self.fc2 };
for (layers) |layer| {
x = zml.call(layer, .forward, .{x});
}
return x.argMax(0, .u8).indices;
}
};const Sdpa = struct {
pub fn forward(_: Sdpa, ctx: *zml.Context, q_: zml.Tensor, k_: zml.Tensor, v_: zml.Tensor) zml.Tensor {
const q = q_.withTags(.{ .b, .h, .q, .hd });
const k = k_.withTags(.{ .b, .h, .k, .hd });
const v = v_.withTags(.{ .b, .h, .k, .hd });
const attn_mask = zml.nn.causalAttnMask(ctx, .{ .q = q.dim(.q), .k = k.dim(.k) }, q.dtype(), null);
return zml.nn.sdpa(ctx, q, k, v, .{ .attn_mask = attn_mask });
}
};You might want to check out more examples, read through the documentation directly on GitHub, or, for the full rendering experience, browse the online documentation with included API reference.
See here.
ZML is licensed under the Apache 2.0 license.
For Tasks:
Click tags to check more tools for each tasksFor Jobs:
Alternative AI tools for zml
Similar Open Source Tools
zml
ZML is a high-performance AI inference stack built for production, using Zig language, MLIR, and Bazel. It allows users to create exciting AI projects, run pre-packaged models like MNIST, TinyLlama, OpenLLama, and Meta Llama, and compile models for accelerator runtimes. Users can also run tests, explore examples, and contribute to the project. ZML is licensed under the Apache 2.0 license.
wllama
Wllama is a WebAssembly binding for llama.cpp, a high-performance and lightweight language model library. It enables you to run inference directly on the browser without the need for a backend or GPU. Wllama provides both high-level and low-level APIs, allowing you to perform various tasks such as completions, embeddings, tokenization, and more. It also supports model splitting, enabling you to load large models in parallel for faster download. With its Typescript support and pre-built npm package, Wllama is easy to integrate into your React Typescript projects.
llama_index
LlamaIndex is a data framework for building LLM applications. It provides tools for ingesting, structuring, and querying data, as well as integrating with LLMs and other tools. LlamaIndex is designed to be easy to use for both beginner and advanced users, and it provides a comprehensive set of features for building LLM applications.
raglite
RAGLite is a Python toolkit for Retrieval-Augmented Generation (RAG) with PostgreSQL or SQLite. It offers configurable options for choosing LLM providers, database types, and rerankers. The toolkit is fast and permissive, utilizing lightweight dependencies and hardware acceleration. RAGLite provides features like PDF to Markdown conversion, multi-vector chunk embedding, optimal semantic chunking, hybrid search capabilities, adaptive retrieval, and improved output quality. It is extensible with a built-in Model Context Protocol server, customizable ChatGPT-like frontend, document conversion to Markdown, and evaluation tools. Users can configure RAGLite for various tasks like configuring, inserting documents, running RAG pipelines, computing query adapters, evaluating performance, running MCP servers, and serving frontends.
Pixel-Reasoner
Pixel Reasoner is a framework that introduces reasoning in the pixel-space for Vision-Language Models (VLMs), enabling them to directly inspect, interrogate, and infer from visual evidences. This enhances reasoning fidelity for visual tasks by equipping VLMs with visual reasoning operations like zoom-in and select-frame. The framework addresses challenges like model's imbalanced competence and reluctance to adopt pixel-space operations through a two-phase training approach involving instruction tuning and curiosity-driven reinforcement learning. With these visual operations, VLMs can interact with complex visual inputs such as images or videos to gather necessary information, leading to improved performance across visual reasoning benchmarks.
suno-api
Suno AI API is an open-source project that allows developers to integrate the music generation capabilities of Suno.ai into their own applications. The API provides a simple and convenient way to generate music, lyrics, and other audio content using Suno.ai's powerful AI models. With Suno AI API, developers can easily add music generation functionality to their apps, websites, and other projects.
Vitron
Vitron is a unified pixel-level vision LLM designed for comprehensive understanding, generating, segmenting, and editing static images and dynamic videos. It addresses challenges in existing vision LLMs such as superficial instance-level understanding, lack of unified support for images and videos, and insufficient coverage across various vision tasks. The tool requires Python >= 3.8, Pytorch == 2.1.0, and CUDA Version >= 11.8 for installation. Users can deploy Gradio demo locally and fine-tune their models for specific tasks.
sieves
sieves is a library for zero- and few-shot NLP tasks with structured generation, enabling rapid prototyping of NLP applications without the need for training. It simplifies NLP prototyping by bundling capabilities into a single library, providing zero- and few-shot model support, a unified interface for structured generation, built-in tasks for common NLP operations, easy extendability, document-based pipeline architecture, caching to prevent redundant model calls, and more. The tool draws inspiration from spaCy and spacy-llm, offering features like immediate inference, observable pipelines, integrated tools for document parsing and text chunking, ready-to-use tasks such as classification, summarization, translation, and more, persistence for saving and loading pipelines, distillation for specialized model creation, and caching to optimize performance.
gateway
Adaline Gateway is a fully local production-grade Super SDK that offers a unified interface for calling over 200+ LLMs. It is production-ready, supports batching, retries, caching, callbacks, and OpenTelemetry. Users can create custom plugins and providers for seamless integration with their infrastructure.
Easy-Translate
Easy-Translate is a script designed for translating large text files with a single command. It supports various models like M2M100, NLLB200, SeamlessM4T, LLaMA, and Bloom. The tool is beginner-friendly and offers seamless and customizable features for advanced users. It allows acceleration on CPU, multi-CPU, GPU, multi-GPU, and TPU, with support for different precisions and decoding strategies. Easy-Translate also provides an evaluation script for translations. Built on HuggingFace's Transformers and Accelerate library, it supports prompt usage and loading huge models efficiently.
jina
Jina is a tool that allows users to build multimodal AI services and pipelines using cloud-native technologies. It provides a Pythonic experience for serving ML models and transitioning from local deployment to advanced orchestration frameworks like Docker-Compose, Kubernetes, or Jina AI Cloud. Users can build and serve models for any data type and deep learning framework, design high-performance services with easy scaling, serve LLM models while streaming their output, integrate with Docker containers via Executor Hub, and host on CPU/GPU using Jina AI Cloud. Jina also offers advanced orchestration and scaling capabilities, a smooth transition to the cloud, and easy scalability and concurrency features for applications. Users can deploy to their own cloud or system with Kubernetes and Docker Compose integration, and even deploy to JCloud for autoscaling and monitoring.
lerobot
LeRobot is a state-of-the-art AI library for real-world robotics in PyTorch. It aims to provide models, datasets, and tools to lower the barrier to entry to robotics, focusing on imitation learning and reinforcement learning. LeRobot offers pretrained models, datasets with human-collected demonstrations, and simulation environments. It plans to support real-world robotics on affordable and capable robots. The library hosts pretrained models and datasets on the Hugging Face community page.
IntelliNode
IntelliNode is a javascript module that integrates cutting-edge AI models like ChatGPT, LLaMA, WaveNet, Gemini, and Stable diffusion into projects. It offers functions for generating text, speech, and images, as well as semantic search, multi-model evaluation, and chatbot capabilities. The module provides a wrapper layer for low-level model access, a controller layer for unified input handling, and a function layer for abstract functionality tailored to various use cases.
next-token-prediction
Next-Token Prediction is a language model tool that allows users to create high-quality predictions for the next word, phrase, or pixel based on a body of text. It can be used as an alternative to well-known decoder-only models like GPT and Mistral. The tool provides options for simple usage with built-in data bootstrap or advanced customization by providing training data or creating it from .txt files. It aims to simplify methodologies, provide autocomplete, autocorrect, spell checking, search/lookup functionalities, and create pixel and audio transformers for various prediction formats.
web-llm
WebLLM is a modular and customizable javascript package that directly brings language model chats directly onto web browsers with hardware acceleration. Everything runs inside the browser with no server support and is accelerated with WebGPU. WebLLM is fully compatible with OpenAI API. That is, you can use the same OpenAI API on any open source models locally, with functionalities including json-mode, function-calling, streaming, etc. We can bring a lot of fun opportunities to build AI assistants for everyone and enable privacy while enjoying GPU acceleration.
llmgraph
llmgraph is a tool that enables users to create knowledge graphs in GraphML, GEXF, and HTML formats by extracting world knowledge from large language models (LLMs) like ChatGPT. It supports various entity types and relationships, offers cache support for efficient graph growth, and provides insights into LLM costs. Users can customize the model used and interact with different LLM providers. The tool allows users to generate interactive graphs based on a specified entity type and Wikipedia link, making it a valuable resource for knowledge graph creation and exploration.
For similar tasks
byteir
The ByteIR Project is a ByteDance model compilation solution. ByteIR includes compiler, runtime, and frontends, and provides an end-to-end model compilation solution. Although all ByteIR components (compiler/runtime/frontends) are together to provide an end-to-end solution, and all under the same umbrella of this repository, each component technically can perform independently. The name, ByteIR, comes from a legacy purpose internally. The ByteIR project is NOT an IR spec definition project. Instead, in most scenarios, ByteIR directly uses several upstream MLIR dialects and Google Mhlo. Most of ByteIR compiler passes are compatible with the selected upstream MLIR dialects and Google Mhlo.
zml
ZML is a high-performance AI inference stack built for production, using Zig language, MLIR, and Bazel. It allows users to create exciting AI projects, run pre-packaged models like MNIST, TinyLlama, OpenLLama, and Meta Llama, and compile models for accelerator runtimes. Users can also run tests, explore examples, and contribute to the project. ZML is licensed under the Apache 2.0 license.
edgeai
Embedded inference of Deep Learning models is quite challenging due to high compute requirements. TI’s Edge AI software product helps optimize and accelerate inference on TI’s embedded devices. It supports heterogeneous execution of DNNs across cortex-A based MPUs, TI’s latest generation C7x DSP, and DNN accelerator (MMA). The solution simplifies the product life cycle of DNN development and deployment by providing a rich set of tools and optimized libraries.
sdnext
SD.Next is an Image Diffusion implementation with advanced features. It offers multiple UI options, diffusion models, and built-in controls for text, image, batch, and video processing. The tool is multiplatform, supporting Windows, Linux, MacOS, nVidia, AMD, IntelArc/IPEX, DirectML, OpenVINO, ONNX+Olive, and ZLUDA. It provides optimized processing with the latest torch developments, including model compile, quantize, and compress functionalities. SD.Next also features Interrogate/Captioning with various models, queue management, automatic updates, and mobile compatibility.
mirage
Mirage Persistent Kernel (MPK) is a compiler and runtime system that automatically transforms LLM inference into a single megakernel—a fused GPU kernel that performs all necessary computation and communication within a single kernel launch. This end-to-end GPU fusion approach reduces LLM inference latency by 1.2× to 6.7×, all while requiring minimal developer effort.
hass-ollama-conversation
The Ollama Conversation integration adds a conversation agent powered by Ollama in Home Assistant. This agent can be used in automations to query information provided by Home Assistant about your house, including areas, devices, and their states. Users can install the integration via HACS and configure settings such as API timeout, model selection, context size, maximum tokens, and other parameters to fine-tune the responses generated by the AI language model. Contributions to the project are welcome, and discussions can be held on the Home Assistant Community platform.
rclip
rclip is a command-line photo search tool powered by the OpenAI's CLIP neural network. It allows users to search for images using text queries, similar image search, and combining multiple queries. The tool extracts features from photos to enable searching and indexing, with options for previewing results in supported terminals or custom viewers. Users can install rclip on Linux, macOS, and Windows using different installation methods. The repository follows the Conventional Commits standard and welcomes contributions from the community.
honcho
Honcho is a platform for creating personalized AI agents and LLM powered applications for end users. The repository is a monorepo containing the server/API for managing database interactions and storing application state, along with a Python SDK. It utilizes FastAPI for user context management and Poetry for dependency management. The API can be run using Docker or manually by setting environment variables. The client SDK can be installed using pip or Poetry. The project is open source and welcomes contributions, following a fork and PR workflow. Honcho is licensed under the AGPL-3.0 License.
For similar jobs
sweep
Sweep is an AI junior developer that turns bugs and feature requests into code changes. It automatically handles developer experience improvements like adding type hints and improving test coverage.
teams-ai
The Teams AI Library is a software development kit (SDK) that helps developers create bots that can interact with Teams and Microsoft 365 applications. It is built on top of the Bot Framework SDK and simplifies the process of developing bots that interact with Teams' artificial intelligence capabilities. The SDK is available for JavaScript/TypeScript, .NET, and Python.
ai-guide
This guide is dedicated to Large Language Models (LLMs) that you can run on your home computer. It assumes your PC is a lower-end, non-gaming setup.
classifai
Supercharge WordPress Content Workflows and Engagement with Artificial Intelligence. Tap into leading cloud-based services like OpenAI, Microsoft Azure AI, Google Gemini and IBM Watson to augment your WordPress-powered websites. Publish content faster while improving SEO performance and increasing audience engagement. ClassifAI integrates Artificial Intelligence and Machine Learning technologies to lighten your workload and eliminate tedious tasks, giving you more time to create original content that matters.
chatbot-ui
Chatbot UI is an open-source AI chat app that allows users to create and deploy their own AI chatbots. It is easy to use and can be customized to fit any need. Chatbot UI is perfect for businesses, developers, and anyone who wants to create a chatbot.
BricksLLM
BricksLLM is a cloud native AI gateway written in Go. Currently, it provides native support for OpenAI, Anthropic, Azure OpenAI and vLLM. BricksLLM aims to provide enterprise level infrastructure that can power any LLM production use cases. Here are some use cases for BricksLLM: * Set LLM usage limits for users on different pricing tiers * Track LLM usage on a per user and per organization basis * Block or redact requests containing PIIs * Improve LLM reliability with failovers, retries and caching * Distribute API keys with rate limits and cost limits for internal development/production use cases * Distribute API keys with rate limits and cost limits for students
uAgents
uAgents is a Python library developed by Fetch.ai that allows for the creation of autonomous AI agents. These agents can perform various tasks on a schedule or take action on various events. uAgents are easy to create and manage, and they are connected to a fast-growing network of other uAgents. They are also secure, with cryptographically secured messages and wallets.
griptape
Griptape is a modular Python framework for building AI-powered applications that securely connect to your enterprise data and APIs. It offers developers the ability to maintain control and flexibility at every step. Griptape's core components include Structures (Agents, Pipelines, and Workflows), Tasks, Tools, Memory (Conversation Memory, Task Memory, and Meta Memory), Drivers (Prompt and Embedding Drivers, Vector Store Drivers, Image Generation Drivers, Image Query Drivers, SQL Drivers, Web Scraper Drivers, and Conversation Memory Drivers), Engines (Query Engines, Extraction Engines, Summary Engines, Image Generation Engines, and Image Query Engines), and additional components (Rulesets, Loaders, Artifacts, Chunkers, and Tokenizers). Griptape enables developers to create AI-powered applications with ease and efficiency.